Abstract

Two techniques of non-parametric change point detection are applied to two different neuroscience datasets. In the first dataset, we show how the multivariate non-parametric change point detection can precisely estimate reaction times to input stimulation in the olfactory system using joint information of spike trains from several neurons. In the second example, we propose to analyze communication and sequence coding using change point formalism as a time segmentation of homogeneous pieces of information, revealing cues to elucidate directionality of the communication in electric fish. We are also sharing our software implementation Chapolins at GitHub.

abstract = "Two techniques of non-parametric change point detection are applied to two different neuroscience datasets. In the first dataset, we show how the multivariate non-parametric change point detection can precisely estimate reaction times to input stimulation in the olfactory system using joint information of spike trains from several neurons. In the second example, we propose to analyze communication and sequence coding using change point formalism as a time segmentation of homogeneous pieces of information, revealing cues to elucidate directionality of the communication in electric fish. We are also sharing our software implementation Chapolins at GitHub.",

N2 - Two techniques of non-parametric change point detection are applied to two different neuroscience datasets. In the first dataset, we show how the multivariate non-parametric change point detection can precisely estimate reaction times to input stimulation in the olfactory system using joint information of spike trains from several neurons. In the second example, we propose to analyze communication and sequence coding using change point formalism as a time segmentation of homogeneous pieces of information, revealing cues to elucidate directionality of the communication in electric fish. We are also sharing our software implementation Chapolins at GitHub.

AB - Two techniques of non-parametric change point detection are applied to two different neuroscience datasets. In the first dataset, we show how the multivariate non-parametric change point detection can precisely estimate reaction times to input stimulation in the olfactory system using joint information of spike trains from several neurons. In the second example, we propose to analyze communication and sequence coding using change point formalism as a time segmentation of homogeneous pieces of information, revealing cues to elucidate directionality of the communication in electric fish. We are also sharing our software implementation Chapolins at GitHub.